Learning Machine Learning - Professional Level

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The Advanced book made the boundaries of a model study inspectable. Professional study means keeping the fitting objective, the evidence target and the decision policy separate: a coefficient can be reproduced for the wrong objective, a probability can be stable but misinterpreted, and a candidate can beat a baseline while failing an absolute requirement. This 55-page guide runs through 20 original units on paper: match the objective before matching the coefficient; use curvature to understand a learning rate; separate sampling noise from a changed gradient; preserve the penalty under a change of units; compute logistic loss without avoidable overflow; know what class weighting does to a probability target; give calibration its own evidence boundary; derive decisions from the actual cost table; allocate a limited capacity by expected benefit; report abstention as part of the system; compare candidates on the same eligible cases; reweight only where evidence has support; bound loss when outcomes are still missing; distinguish local perturbation bounds from global safety; explain reliance without calling it causality; keep slice denominators and harms visible; test the feature contract across environments; verify decision parity as well as numeric tolerance; separate an alert from a measured quality failure; and a capstone closing a review with a conditional decision intact. Every unit has a lesson and a workshop with a worked case, safe activity and explained answers. Every workshop uses invented records on paper, with optional harmless offline arithmetic - no machine-learning package, online service or production system is required, and no live account, personal data or automated action is needed or authorised. The final review keeps both an unresolved strict loss condition and a known decision-parity failure on the page: a clearly documented not-ready decision can be the right finished outcome. Best suited to readers who have finished the Advanced edition or are